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Architecture · Version 1.0.0 · Reviewed 2026-08-02

API Error Taxonomy Designer

Make a defensible decision about code taxonomy and retryability signaling with evidence, explicit trade-offs, and a verification plan.

4 method steps 4 documented failure modes 4 diagnostic checks 7 quality gates

Designs machine-readable error codes so clients can act differently without parsing prose.

₹99 one-time

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What this skill helps you do

  • Code taxonomy
  • Retryability signaling
  • Client guidance

How API Error Taxonomy Designer works

You provide

Requirements, constraints, and the current topology

It inspects

Critical path and failure boundaries for code taxonomy

It decides

A retryability signaling decision with consequences recorded

You verify

Rollout stages with the signal that gates each one

What it checks first

API Error Taxonomy Designer designs machine-readable error codes so clients can act differently without parsing prose. Use it when the work involves Code taxonomy, Retryability signaling, Client guidance.

  1. The quality attribute that actually constrains the design: latency, consistency, availability, cost, or compliance.
  2. The critical path and the number of network hops on it.
  3. Where state lives and who owns it, since ownership ambiguity becomes a correctness problem.
  4. The failure behavior of every dependency: fail open, fail closed, or degrade.

Failure modes it recognizes

  • Synchronous coupling making availability the product of all dependency availabilities.
  • A shared database creating hidden coupling between nominally independent services.
  • A component with no clear owner, so its failure has no defined response.
  • Distributed transactions attempted across services without a saga or compensation model.

Answers it will reject

  • Selecting a technology before establishing the constraint it is meant to satisfy.
  • Presenting a diagram as a design without the failure and data-consistency model.
  • Optimizing for a hypothetical future scale at the cost of present operability.

Decision rules it applies

  • Make the consistency requirement explicit per operation, not per system.
  • Prefer designs whose failure modes are understood over designs whose peak performance is higher.
  • Record the decision, the rejected alternatives, and the conditions that would reverse it.

Evidence it asks for

  • Quantify load, growth, and latency budget with arithmetic and stated assumptions.
  • Define the rollout stages and the signal that gates each one.
  • Name the reversal path for the decision.

The method inside

  1. Turn code taxonomy into explicit functional requirements and quality-attribute constraints.
  2. Model the critical path, state, trust, and failure boundaries that govern retryability signaling.
  3. Compare viable designs for client guidance against weighted constraints and operational ownership.
  4. Select a design with consequences, rollout stages, observability, and a reversible adoption path.

Deliverables

  • Code taxonomy assessment
  • Retryability signaling decision and action plan
  • Client guidance verification checklist

Evidence requirements

  • Functional and quality requirements
  • Scale, latency, consistency, cost, and compliance constraints
  • Current topology and alternatives considered

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Clients cannot tell our transient failures from permanent ones because every error returns the same shape.

Expected output

Without a stable code, clients either retry everything or nothing, and both are wrong. Add a machine-readable code plus an explicit retryable flag, keep the human message separate, and document which codes are safe to retry and with what backoff...

Boundaries and compatibility

Ideal for

  • Code taxonomy: produce a decision or artifact grounded in supplied evidence.
  • Retryability signaling: produce a decision or artifact grounded in supplied evidence.
  • Client guidance: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Producing a generic reference architecture without requirements
  • Hiding material trade-offs behind best-practice language

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.